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This paper presents a Genetic Algorithm (GA) based wrapper feature selection method for classifying hyperspectral images using Support Vector Machine (SVM). The proposed method optimizes both feature subsets and SVM kernel parameters simultaneously, resulting in a reduction of the number of bands used from 198 to 13 and an increase in classification accuracy from 88.81% to 92.51%. The GA-SVM method demonstrates improved computational efficiency and accuracy in hyperspectral data classification compared to t

Author

lucas mart

Language

EN